Investigators often go to great lengths to obtain careful, detailed measures of exposure, which may have multiple dimensions and may change over time, e.g. diet, stress, or blood pressure. In assessing the association between an individual's exposure history and the time or rate of occurrence of a health event, it is important to reduce the dimensionality of this multivariate exposure history data in order to increase statistical power. Although replacing the multivariate exposure information with a simple summary, as is typically done in practice, can sometimes improve interpretability and statistical power, it is typically not clear how best to summarize the information at hand. In addition, reducing detailed data into naive summaries often runs counter to the study goals of obtaining the most accurate assessment of exposure possible. We are interested in developing and applying statistical methods, allowing evidence-based summaries to be constructed objectively in a manner that maximizes information about the outcomes of interest. Motivated by data from the Pregnancy, Infection, and Nutrition (PIN) study, a prospective cohort study of preterm birth, we propose a Bayesian hierarchical model for a multiple episode exposure process and a reproductive outcome. Data on timing, duration, and intensity of exposure are summarized using a shared frailty term within a framework that accounts for changes in the process over time, correlated exposure measures, and missing data. Inferences on exposure effects can be based on the posterior density obtained using an efficient MCMC algorithm. The methods will be applied to vaginal bleeding and duration of gestation using the Pregnancy, Infection, and Nutrition study data but have broad applications in reproductive health, epidemiology, and other areas.